%0 Journal Article
%T Machine Learning Versus Logistic Regression Methods for 2-Year Mortality Prognostication in a Small, Heterogeneous Glioma Database
%A Fang-Cheng Yeh
%A Juan C. Fernandez-Miranda
%A Rhett N. D'Souza
%A Sandip S. Panesar
%J Archive of "World Neurosurgery: X".
%D 2019
%R 10.1016/j.wnsx.2019.100012
%X Machine learning (ML) is the application of specialized algorithms to datasets for trend delineation, categorization, or prediction. ML techniques have been traditionally applied to large, highly dimensional databases. Gliomas are a heterogeneous group of primary brain tumors, traditionally graded using histopathologic features. Recently, the World Health Organization proposed a novel grading system for gliomas incorporating molecular characteristics. We aimed to study whether ML could achieve accurate prognostication of 2-year mortality in a small, highly dimensional database of patients with glioma
%K Diagnosis
%K Gliomas
%K Logistic regression
%K Machine learning
%K Neuro-oncology
%K Prognostication ANN
%K Artificial neural network
%K AUC
%K Area under the curve
%K CI
%K Confidence interval
%K DT
%K Decision tree
%K LR
%K Logistic regression
%K ML
%K Machine learning
%K NLR
%K Negative likelihood ratio
%K NPV
%K Negative predictive value
%K PLR
%K Positive likelihood ratio
%K PPV
%K Positive predictive value
%K SVM
%K Support vector machine
%K WHO
%K World Health Organization
%U https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6581022/